Pandas Profiling Report

Dataset statistics

Number of variables	16
Number of observations	3351
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	396.2 KiB
Average record size in memory	121.1 B

Variable types

DateTime	1
Categorical	3
Numeric	12

Warnings

`username` has constant value "JohnLegere"	Constant
`tweet` has a high cardinality: 3351 distinct values	High cardinality
`mentions` is highly correlated with `hashtags` and 1 other fields	High correlation
`hashtags` is highly correlated with `mentions` and 2 other fields	High correlation
`video` is highly correlated with `hashtags` and 2 other fields	High correlation
`photos` is highly correlated with `video` and 1 other fields	High correlation
`number of tweets` is highly correlated with `mentions` and 3 other fields	High correlation
`mentions` is highly correlated with `hashtags` and 7 other fields	High correlation
`hashtags` is highly correlated with `mentions` and 7 other fields	High correlation
`video` is highly correlated with `mentions` and 6 other fields	High correlation
`photos` is highly correlated with `mentions` and 5 other fields	High correlation
`urls` is highly correlated with `retweets_count`	High correlation
`replies_count` is highly correlated with `mentions` and 5 other fields	High correlation
`retweets_count` is highly correlated with `mentions` and 8 other fields	High correlation
`likes_count` is highly correlated with `mentions` and 6 other fields	High correlation
`number of tweets` is highly correlated with `mentions` and 7 other fields	High correlation
`price` is highly correlated with `mentions` and 3 other fields	High correlation
`mentions` is highly correlated with `hashtags` and 2 other fields	High correlation
`hashtags` is highly correlated with `mentions` and 2 other fields	High correlation
`video` is highly correlated with `photos` and 1 other fields	High correlation
`photos` is highly correlated with `video` and 1 other fields	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `mentions` and 3 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`number of tweets` is highly correlated with `mentions` and 3 other fields	High correlation
`urls` is highly correlated with `mentions`	High correlation
`replies_count` is highly correlated with `retweets_count`	High correlation
`video` is highly correlated with `photos` and 2 other fields	High correlation
`mentions` is highly correlated with `urls` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`photos` is highly correlated with `video` and 2 other fields	High correlation
`bins` is highly correlated with `percent change`	High correlation
`number of tweets` is highly correlated with `video` and 2 other fields	High correlation
`price` is highly correlated with `hashtags`	High correlation
`hashtags` is highly correlated with `video` and 4 other fields	High correlation
`likes_count` is highly correlated with `retweets_count`	High correlation
`percent change` is highly correlated with `bins`	High correlation
`bins` is highly correlated with `username`	High correlation
`username` is highly correlated with `bins`	High correlation
`retweets_count` is highly skewed (γ1 = 24.98091088)	Skewed
`tweet` is uniformly distributed	Uniform
`date` has unique values	Unique
`tweet` has unique values	Unique
`mentions` has 1017 (30.3%) zeros	Zeros
`hashtags` has 771 (23.0%) zeros	Zeros
`cashtags` has 3275 (97.7%) zeros	Zeros
`video` has 670 (20.0%) zeros	Zeros
`photos` has 787 (23.5%) zeros	Zeros
`urls` has 613 (18.3%) zeros	Zeros
`percent change` has 38 (1.1%) zeros	Zeros

Reproduction

Analysis started	2021-09-27 19:04:49.067099
Analysis finished	2021-09-27 19:05:10.249818
Duration	21.18 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

date
Date

UNIQUE

Distinct	3351
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	26.3 KiB

Minimum	2016-08-23 09:30:00
Maximum	2021-07-20 16:00:00

Histogram

Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	3351
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	26.3 KiB

@Azphxmex @art_lucero - please look into it ASAP @mgcarley @TMobile You’re wearing the wrong color ;) @tsukinoxo @TMobile do it!!! @mikusz @ATT @TMobile @ATTCares Good choice :)))) @Abruuh_ @TMobile Welcome back! Ready to #GetThanked?! @Mikesmith2317 Come on over!! @Prttyawsmcool Email me! John.legere@t-mobile.com Well, there goes my productivity for the day https://t.co/vxZTn2raCG The future of watching the #Olympics is on mobile, but I think we already knew that! ;) https://t.co/FCsSqRPJMN @isnotgraham @art_lucero john.legere@t-mobile.com @isnotgraham @art_lucero can you email me? Oooo, I need this! @thenorthface, how do I get one in #magenta?!? #gadgetlover https://t.co/xWKZXcSd5X More things to #BingeOn from @Netflix!! https://t.co/Y8IikGMJH9 Hey @MichaelPhelps - I've got your back!! :)))) Watch this: https://t.co/B1XR0MHSjk https://t.co/vnsLUmLuCg Interesting arguments for/against wearables on the field. #MLB https://t.co/IRraM3onkr Use these tips to get tickets, and @tmobile to get free wifi while you’re flying. #tmobilewingman https://t.co/jwffPWCP9F	1
@ThePudgeyFellow @TMobile Never too early to build good habits😆 Happy birthday @DavidCarey! Tuesdays would not be the same without you! https://t.co/999SyPCt9c @Chris__Michaels Enjoy! @EricTKinman @TMobile enjoy!! @Colleen_Clare @Megan_Rosebud @TMobile enjoy!	1
@halohuh https://t.co/UjuXdZnwmv @alittleharrisy @TMobile @UW @tmobilecareers 🙌 @mstrongjr 🤔 @women_yang https://t.co/OjSGTgiWJb There’s really no bad pizza but this is the perfect combo in my opinion! What’s your go-to pizza topping? 🍕 https://t.co/mjZvg6ojoL GREAT 🙄 Who let @verHIDEzon buy ad space outside our NY Signature Store⁉️ #5GDoneWrong https://t.co/7XkdfM9hcS @s_brownie @AuntieAnnes @TMobile https://t.co/hFyxRDtvVf THIS IS ABSURD! These guys even have a twitter account?! 🤦‍♂️🤦‍♂️🤦‍♂️ @VerHIDEzon https://t.co/Hn0AiQugRT @majeikstagram @BKaminsky https://t.co/dd9mrLQTpB @Samuelg33791554 you're forgiven @millionmiler24 https://t.co/yg5uF7tyoa @laughlovego @TMobile @AuntieAnnes enjoy! Have you seen this new company verHIDEzon? Looks like they are forcing their customers to pay more for 5G… but not telling them where they can find it? Sounds familiar… #5GDoneWrong https://t.co/X6MhEOH1GI @Samuelg33791554 https://t.co/4m6fHsr9Ok	1
Something had to be done. @Verizon’s been talking a big game about 5G, charging more for it and not even giving customers a map to find it. Hopefully all of NYC (and the rest of the country) now realizes @verHIDEzon = Verizon 👀 @MusicalMoore6 @TMobile @TMobileHelp https://t.co/qxaepJiKKR @jvkap @TMobile Go team! 🎉 In 51 hours, the new #iPhone11, #iPhone11Pro and #iPhone11ProMax can be seen by your very own eyes 👀 🛸 #StormTMobile https://t.co/26TaNwcDLb @MarcSaint9 K. @Cyandashie https://t.co/GkwDbBC964	1
@joecro1984 https://t.co/U5qs9qst4p @xurvisgaming https://t.co/4n2W62S6Pp @in4birdie 🙌🏼🙌🏼🙌🏼 @Big_Problems_ https://t.co/e93zg4BQcL @riskybiz64 https://t.co/7dJVTjmHUz I was told I shouldn’t post this... https://t.co/9psoqgF7DM @yeetus_meetus @KoboldCrusader https://t.co/rk7ycuRkX6 @Jack_bibi tell everyone you meet to join @TMobile!!!	1
Other values (3346)	3346

Length

Max length	7236
Median length	666
Mean length	830.9755297
Min length	23

Characters and Unicode

Total characters	2784599
Distinct characters	798
Distinct categories	21 ?
Distinct scripts	6 ?
Distinct blocks	21 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3351 ?
Unique (%)	100.0%

Sample

1st row	@Azphxmex @art_lucero - please look into it ASAP @mgcarley @TMobile You’re wearing the wrong color ;) @tsukinoxo @TMobile do it!!! @mikusz @ATT @TMobile @ATTCares Good choice :)))) @Abruuh_ @TMobile Welcome back! Ready to #GetThanked?! @Mikesmith2317 Come on over!! @Prttyawsmcool Email me! John.legere@t-mobile.com Well, there goes my productivity for the day https://t.co/vxZTn2raCG The future of watching the #Olympics is on mobile, but I think we already knew that! ;) https://t.co/FCsSqRPJMN @isnotgraham @art_lucero john.legere@t-mobile.com @isnotgraham @art_lucero can you email me? Oooo, I need this! @thenorthface, how do I get one in #magenta?!? #gadgetlover https://t.co/xWKZXcSd5X More things to #BingeOn from @Netflix!! https://t.co/Y8IikGMJH9 Hey @MichaelPhelps - I've got your back!! :)))) Watch this: https://t.co/B1XR0MHSjk https://t.co/vnsLUmLuCg Interesting arguments for/against wearables on the field. #MLB https://t.co/IRraM3onkr Use these tips to get tickets, and @tmobile to get free wifi while you’re flying. #tmobilewingman https://t.co/jwffPWCP9F
2nd row	@silods yes! Very proud of @UMassAmherst and to have studied there! https://t.co/sjK4IbTy7z @iSocialFanz possibly :)) This is hilarious, but also brilliant!! https://t.co/9wtLj2J0qa I might wear one if it were #magenta, @Xbox…. ;) https://t.co/Mk3EI3Q37R #TuesdayMotivation: Today is #TMobileTuesdays & you can #GetThanked w/ awesome prizes from @Orbitz!! #TMobileTuesdays is giving away a chance to win tickets to anywhere in North America!! Where would u go?!? https://t.co/8FGkPmbUYG
3rd row	@Woofdah @TMobile What are you going to rent?! @FreelancerJenny 👍🏼 @CURLZ_92 The best day of the week!! @SCAFFBEEZYjr They should switch! @lennahs @TMobile good choice! I think this means I technically have a gold medal ;) https://t.co/CC9KXb07Ch Add this to the list of ways not to impress a date https://t.co/gIGqMvBd1t So great!! You need to hear what we’re cooking up for @TMobileAtWork customers, I think you’d like it! https://t.co/lTCXDP5ero Watching #TheProfit, love the show. @MarcusLemonis does this shirt come in magenta?! https://t.co/S262GTf1OQ @irusanovschi stop thinking and come over!!!! @_Lucky45 i read all email and follow up... No boiler rooms @TMobile :) It’s #TravelTuesday & #TMobileTuesdays!! Coincidence?? Nope!! Now, go try to win cool @Orbitz stuff!! I wonder what would come up if you draw @ATT?! #DeatthStar LOL https://t.co/MvuHgF7QxD Today I dress in what I love! All @TMobile (well I do every day)! ...it's also a great way to meet people :)) https://t.co/iqQAaTLXnm Flying out of Seattle on a beautiful day... Many stops ahead in next week..a lot of @TMobile people to thank! https://t.co/IQ5n8Yz6L6
4th row	Sunscreen doesn’t smell great, but I don’t think fried chicken-scented sunscreen is any better… https://t.co/VT6qgGquMK Our thoughts & prayers are with 🇮🇹. Fees for @TMobile custs to call/text to/from Italy will be waived until 8/28. https://t.co/wcw2ZY98aL I read every email I get, no matter what :))))) https://t.co/AURnzcFZV5 Is this what the future looks like??? https://t.co/3KU7cnqv5f
5th row	@MillerPhelan Thank you!! @LIVEWlRED You should! @Beverly330 @TMobile welcome!! @petersterne I ❤️ @TMobile ;) @ArimHerrera Switch to @TMobile! :)))) @ItsTonyWhite There’s always next week! @AlienReconstar Set reminders! ;) Great day for a run in @CentralParkNYC of course in all @TMobile #Magenta https://t.co/pK5yZZQZmB

Common Values

Value	Count	Frequency (%)
@Azphxmex @art_lucero - please look into it ASAP @mgcarley @TMobile You’re wearing the wrong color ;) @tsukinoxo @TMobile do it!!! @mikusz @ATT @TMobile @ATTCares Good choice :)))) @Abruuh_ @TMobile Welcome back! Ready to #GetThanked?! @Mikesmith2317 Come on over!! @Prttyawsmcool Email me! John.legere@t-mobile.com Well, there goes my productivity for the day https://t.co/vxZTn2raCG The future of watching the #Olympics is on mobile, but I think we already knew that! ;) https://t.co/FCsSqRPJMN @isnotgraham @art_lucero john.legere@t-mobile.com @isnotgraham @art_lucero can you email me? Oooo, I need this! @thenorthface, how do I get one in #magenta?!? #gadgetlover https://t.co/xWKZXcSd5X More things to #BingeOn from @Netflix!! https://t.co/Y8IikGMJH9 Hey @MichaelPhelps - I've got your back!! :)))) Watch this: https://t.co/B1XR0MHSjk https://t.co/vnsLUmLuCg Interesting arguments for/against wearables on the field. #MLB https://t.co/IRraM3onkr Use these tips to get tickets, and @tmobile to get free wifi while you’re flying. #tmobilewingman https://t.co/jwffPWCP9F	1	< 0.1%
@ThePudgeyFellow @TMobile Never too early to build good habits😆 Happy birthday @DavidCarey! Tuesdays would not be the same without you! https://t.co/999SyPCt9c @Chris__Michaels Enjoy! @EricTKinman @TMobile enjoy!! @Colleen_Clare @Megan_Rosebud @TMobile enjoy!	1	< 0.1%
@halohuh https://t.co/UjuXdZnwmv @alittleharrisy @TMobile @UW @tmobilecareers 🙌 @mstrongjr 🤔 @women_yang https://t.co/OjSGTgiWJb There’s really no bad pizza but this is the perfect combo in my opinion! What’s your go-to pizza topping? 🍕 https://t.co/mjZvg6ojoL GREAT 🙄 Who let @verHIDEzon buy ad space outside our NY Signature Store⁉️ #5GDoneWrong https://t.co/7XkdfM9hcS @s_brownie @AuntieAnnes @TMobile https://t.co/hFyxRDtvVf THIS IS ABSURD! These guys even have a twitter account?! 🤦‍♂️🤦‍♂️🤦‍♂️ @VerHIDEzon https://t.co/Hn0AiQugRT @majeikstagram @BKaminsky https://t.co/dd9mrLQTpB @Samuelg33791554 you're forgiven @millionmiler24 https://t.co/yg5uF7tyoa @laughlovego @TMobile @AuntieAnnes enjoy! Have you seen this new company verHIDEzon? Looks like they are forcing their customers to pay more for 5G… but not telling them where they can find it? Sounds familiar… #5GDoneWrong https://t.co/X6MhEOH1GI @Samuelg33791554 https://t.co/4m6fHsr9Ok	1	< 0.1%
Something had to be done. @Verizon’s been talking a big game about 5G, charging more for it and not even giving customers a map to find it. Hopefully all of NYC (and the rest of the country) now realizes @verHIDEzon = Verizon 👀 @MusicalMoore6 @TMobile @TMobileHelp https://t.co/qxaepJiKKR @jvkap @TMobile Go team! 🎉 In 51 hours, the new #iPhone11, #iPhone11Pro and #iPhone11ProMax can be seen by your very own eyes 👀 🛸 #StormTMobile https://t.co/26TaNwcDLb @MarcSaint9 K. @Cyandashie https://t.co/GkwDbBC964	1	< 0.1%
@joecro1984 https://t.co/U5qs9qst4p @xurvisgaming https://t.co/4n2W62S6Pp @in4birdie 🙌🏼🙌🏼🙌🏼 @Big_Problems_ https://t.co/e93zg4BQcL @riskybiz64 https://t.co/7dJVTjmHUz I was told I shouldn’t post this... https://t.co/9psoqgF7DM @yeetus_meetus @KoboldCrusader https://t.co/rk7ycuRkX6 @Jack_bibi tell everyone you meet to join @TMobile!!!	1	< 0.1%
@LmtlessLi @TMobileArena Congrats! You're getting two 🎟 to @iHeartFestival! @TMobileHelp will reach out to get your details. @adrianrosales Congrats! You're getting two 🎟 to @iHeartFestival! @TMobileHelp will reach out to get your details. @joshrobertnay @BurgerKing you can do that too! @Torre_Green I just want to be friends Torre @YOUNGSHINE2 @BurgerKing but they're real @Resqme29445 Do it! Like I always say... Shut up and listen to your customers!! You told us you wanted the @BurgerKing 👑 Impossible Whopper and now it's yours! Get your FREE Impossible Whopper 🍔 on next week's #TMobileTuesdays https://t.co/J1UCdLuO5K @hypno_squid this is a good one!😂😂 @JoeCool205 @TMobileHelp @SokieSok https://t.co/7N0F4MuAXx	1	< 0.1%
@BlackNGoldBunny @BurgerKing https://t.co/w8OyBUtmYS @BlackNGoldBunny @BurgerKing https://t.co/H8mXdsGD1K Happy birthday @JimmyFallon! I made you a #SlowCookerSunday cake! 😂🎂 #smile https://t.co/CBtweVUefD @GucciRatz https://t.co/NTCAnS9a1r @Actual_Andre @VerHIDEzon 😉 @jaredmroberts @StaneffMatt 🤔🤔🤔 There are so many incredible women that make @TMobile the amazing place it is to work and I couldn’t be more proud to have made @Fortunes list!	1	< 0.1%
@JeanneMallett @TMobile great photo! @MignonClyburn I’m sorry for your immeasurable loss. My prayers go out to you, @WhipClyburn and your family. She was an amazing woman who leaves us a remarkable legacy. As you can 👀 the real party is at @TMobile!! 🎉 The new iPhones have landed 🛸📱Use #StormTMobile to share why this phone is OUT OF THIS WORLD and I’ll pick my favorite to win one👽 @ErickG3m1n1 @TMobile woo hoo!!! @napier03 @TMobile @BurgerKing https://t.co/Q872cT129j So who’s going? Bring me back a UFO! #Area51 https://t.co/QqlqxanpSF They're hereeeeee 🛸📱#StormTMobile NOW to get your new #iPhone11, #iPhone11Pro or #iPhone11ProMax!! https://t.co/Jwt9L2uLBR @iamalyxander @TMobile @TMobileHelp Email me john.legere@t-mobile.com @IamVeRoNiCaG @TMobile nice! enjoy! @CoreyTa32653536 https://t.co/VF25U87aXq @JeffChausse Do not believe this. @StevenCastroJr1 @AguirreSaralily @TMobile @Shell 🙌🏼🙌🏼🙌🏼	1	< 0.1%
@mrbrauer I couldn't agree more 🤩 Thank you A.B. for sharing your story with the world – your courage and honesty is an inspiration! #WeAreTMobile I admire the effort here, but do kids these days know what string cheese is? They do come in all flavors….check your local grocery store…😂 https://t.co/e9n0gDhAQj @Isabell22107618 @TMobile @ATT @verizon 😁😬😁 @Bandk4me @TMobile woo hoo! welcome to @TMobile https://t.co/rrXeMKIenG Happy #RuffFriday!! Meet Lala, an adorable 2-year-old pup who loves a good ol’ game of fetch and being with her people. She’s looking for a home where her future family will be around a lot to cuddle and play with her. Head to @SeattleHumane to meet her today! @aidenjamestour enjoy! @vgkhozier for you Jess, I do. 👀	1	< 0.1%
@Arg1031NY @TMobile You win! But you forgot to mention our newest, most powerful 600 MHz spectrum 😉 @TMobileHelp will reach out to get your info. @JaimeTPA @TMobile You win! iPhone 11 + T-Mobile Network + Winning one for FREE = win/win/win!! @TMobileHelp will reach out to get your info. CONGRATS to our #runscope #BatmanDay winners @Brandonsarsori1, @Truthster11 and @SOSpleaz for winning an Apple Watch Series 5 and @AbuSamak for winning an iPhone 11 (or Apple Watch Series 5). Good job on knowing so much about me (Batman)!!! #LIVE @TMobile CEO: #Runscope and #applewatch #giveaway time from @CentralParkNYC on #BatmanDay! Join me NOW! https://t.co/jyUS4Hobr2 I am on a run @CentralParkNYC ... maybe it’s time for a #Runscope #BatmanDay #Trivia #Giveaway ? https://t.co/oxYJr012qe It’s the last weekend of summer, CAN YOU BELIEVE IT!? How are you taking advantage of the last weekend in summer? Ridin’ around in my batmobile! Happy ME day everyone!! #BatmanDay #IAmBatman https://t.co/njdJrfdpFu	1	< 0.1%
Other values (3341)	3341	99.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
tmobile	11032	3.0%
to	9884	2.7%
the	8284	2.2%
you	6812	1.8%
a	4975	1.3%
and	4663	1.3%
i	4651	1.3%
	4436	1.2%
for	3828	1.0%
it	3411	0.9%
Other values (58664)	307002	83.2%

Most occurring characters

Value	Count	Frequency (%)
	385250	13.8%
e	206196	7.4%
t	174423	6.3%
o	171214	6.1%
a	128729	4.6%
i	116363	4.2%
s	102832	3.7%
n	102647	3.7%
r	95040	3.4%
l	88340	3.2%
Other values (788)	1213565	43.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	1779381	63.9%
Space Separator	385553	13.8%
Uppercase Letter	268304	9.6%
Other Punctuation	239406	8.6%
Decimal Number	67151	2.4%
Other Symbol	15766	0.6%
Final Punctuation	7759	0.3%
Connector Punctuation	5822	0.2%
Dash Punctuation	4471	0.2%
Close Punctuation	3671	0.1%
Other values (11)	7315	0.3%

Most frequent character per category

Other Symbol

Value	Count	Frequency (%)
🙌	2076	13.2%
😂	1358	8.6%
😉	1019	6.5%
👍	591	3.7%
😎	377	2.4%
🎉	353	2.2%
🤔	338	2.1%
✌	325	2.1%
😊	306	1.9%
😁	281	1.8%
Other values (643)	8742	55.4%

Uppercase Letter

Value	Count	Frequency (%)
T	33388	12.4%
M	26639	9.9%
S	15918	5.9%
I	15254	5.7%
A	12701	4.7%
C	11678	4.4%
W	11296	4.2%
E	10318	3.8%
L	10027	3.7%
D	9717	3.6%
Other values (33)	111368	41.5%

Lowercase Letter

Value	Count	Frequency (%)
e	206196	11.6%
t	174423	9.8%
o	171214	9.6%
a	128729	7.2%
i	116363	6.5%
s	102832	5.8%
n	102647	5.8%
r	95040	5.3%
l	88340	5.0%
h	79793	4.5%
Other values (20)	513804	28.9%

Other Punctuation

Value	Count	Frequency (%)
@	58062	24.3%
/	52367	21.9%
!	38549	16.1%
.	33654	14.1%
:	20005	8.4%
#	12237	5.1%
?	7142	3.0%
,	6439	2.7%
;	3523	1.5%
'	2922	1.2%
Other values (9)	4506	1.9%

Decimal Number

Value	Count	Frequency (%)
1	10700	15.9%
2	8392	12.5%
0	7987	11.9%
3	6166	9.2%
5	5934	8.8%
4	5871	8.7%
7	5767	8.6%
8	5586	8.3%
9	5563	8.3%
6	5185	7.7%

Modifier Symbol

Value	Count	Frequency (%)
🏼	2271	67.8%
🏻	938	28.0%
¯	60	1.8%
🏽	51	1.5%
🏿	17	0.5%
^	7	0.2%
￣	5	0.1%
´	3	0.1%

Math Symbol

Value	Count	Frequency (%)
+	259	48.8%
=	224	42.2%
◻	16	3.0%
~	14	2.6%
\|	14	2.6%
⤵	2	0.4%
↔	1	0.2%
↓	1	0.2%

Space Separator

Value	Count	Frequency (%)
	385250	99.9%
	296	0.1%
	6	< 0.1%
	1	< 0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	4010	89.7%
–	456	10.2%
—	5	0.1%

Final Punctuation

Value	Count	Frequency (%)
’	7395	95.3%
”	363	4.7%
»	1	< 0.1%

Format

Value	Count	Frequency (%)
‍	375	99.5%
⁦	1	0.3%
⁩	1	0.3%

Connector Punctuation

Value	Count	Frequency (%)
_	5818	99.9%
＿	4	0.1%

Close Punctuation

Value	Count	Frequency (%)
)	3670	> 99.9%
]	1	< 0.1%

Open Punctuation

Value	Count	Frequency (%)
(	553	99.8%
[	1	0.2%

Initial Punctuation

Value	Count	Frequency (%)
“	363	88.1%
‘	49	11.9%

Nonspacing Mark

Value	Count	Frequency (%)
️	1255	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	752	100.0%

Other Number

Value	Count	Frequency (%)
½	1	100.0%

Enclosing Mark

Value	Count	Frequency (%)
⃣	50	100.0%

Other Letter

Value	Count	Frequency (%)
ツ	30	100.0%

Private Use

Value	Count	Frequency (%)
	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2047662	73.5%
Common	735207	26.4%
Inherited	1680	0.1%
Katakana	30	< 0.1%
Braille	19	< 0.1%
Unknown	1	< 0.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	385250	52.4%
@	58062	7.9%
/	52367	7.1%
!	38549	5.2%
.	33654	4.6%
:	20005	2.7%
#	12237	1.7%
1	10700	1.5%
2	8392	1.1%
0	7987	1.1%
Other values (726)	108004	14.7%

Latin

Value	Count	Frequency (%)
e	206196	10.1%
t	174423	8.5%
o	171214	8.4%
a	128729	6.3%
i	116363	5.7%
s	102832	5.0%
n	102647	5.0%
r	95040	4.6%
l	88340	4.3%
h	79793	3.9%
Other values (46)	782085	38.2%

Inherited

Value	Count	Frequency (%)
️	1255	74.7%
‍	375	22.3%
⃣	50	3.0%

Katakana

Value	Count	Frequency (%)
ツ	30	100.0%

Unknown

Value	Count	Frequency (%)
	1	100.0%

Braille

Value	Count	Frequency (%)
⠀	19	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2753063	98.9%
Punctuation	10736	0.4%
None	10449	0.4%
Emoticons	6871	0.2%
VS	1255	< 0.1%
Dingbats	842	< 0.1%
Misc Symbols	727	< 0.1%
Latin 1 Sup	368	< 0.1%
Enclosed Alphanum Sup	148	< 0.1%
Misc Technical	38	< 0.1%
Other values (11)	102	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	385250	14.0%
e	206196	7.5%
t	174423	6.3%
o	171214	6.2%
a	128729	4.7%
i	116363	4.2%
s	102832	3.7%
n	102647	3.7%
r	95040	3.5%
l	88340	3.2%
Other values (80)	1182029	42.9%

Punctuation

Value	Count	Frequency (%)
’	7395	68.9%
…	1648	15.4%
–	456	4.2%
‍	375	3.5%
“	363	3.4%
”	363	3.4%
‘	49	0.5%
‼	44	0.4%
•	16	0.1%
⁉	14	0.1%
Other values (4)	13	0.1%

None

Value	Count	Frequency (%)
🏼	2271	21.7%
🏻	938	9.0%
👍	591	5.7%
🎉	353	3.4%
🤔	338	3.2%
📱	250	2.4%
👀	195	1.9%
🏃	179	1.7%
💖	156	1.5%
🐶	147	1.4%
Other values (503)	5031	48.1%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇺	41	27.7%
🇸	40	27.0%
🆓	29	19.6%
🇵	8	5.4%
🇮	5	3.4%
🇹	5	3.4%
🇷	5	3.4%
🆕	4	2.7%
🇭	3	2.0%
🇩	2	1.4%
Other values (5)	6	4.1%

Dingbats

Value	Count	Frequency (%)
✌	325	38.6%
❤	209	24.8%
✅	126	15.0%
❄	54	6.4%
✈	52	6.2%
✔	23	2.7%
✨	14	1.7%
➡	6	0.7%
✍	5	0.6%
❗	5	0.6%
Other values (10)	23	2.7%

VS

Value	Count	Frequency (%)
️	1255	100.0%

Emoticons

Value	Count	Frequency (%)
🙌	2076	30.2%
😂	1358	19.8%
😉	1019	14.8%
😎	377	5.5%
😊	306	4.5%
😁	281	4.1%
😍	182	2.6%
😏	153	2.2%
😱	101	1.5%
😅	92	1.3%
Other values (54)	926	13.5%

Misc Symbols

Value	Count	Frequency (%)
♂	224	30.8%
⚾	113	15.5%
☕	55	7.6%
☺	49	6.7%
☀	46	6.3%
⚡	41	5.6%
♀	37	5.1%
☝	19	2.6%
☔	15	2.1%
♻	13	1.8%
Other values (26)	115	15.8%

Latin 1 Sup

Value	Count	Frequency (%)
	296	80.4%
¯	60	16.3%
é	4	1.1%
´	3	0.8%
½	1	0.3%
ä	1	0.3%
è	1	0.3%
»	1	0.3%
ñ	1	0.3%

Playing Cards

Value	Count	Frequency (%)
🃏	1	100.0%

Misc Technical

Value	Count	Frequency (%)
⌚	22	57.9%
⏰	7	18.4%
⏲	4	10.5%
⏱	3	7.9%
⌨	1	2.6%
⏳	1	2.6%

Katakana

Value	Count	Frequency (%)
ツ	30	100.0%

Specials

Value	Count	Frequency (%)
�	4	100.0%

PUA

Value	Count	Frequency (%)
	1	100.0%

Arrows

Value	Count	Frequency (%)
↔	1	50.0%
↓	1	50.0%

Braille

Value	Count	Frequency (%)
⠀	19	100.0%

Geometric Shapes

Value	Count	Frequency (%)
◻	16	100.0%

Math Alphanum

Value	Count	Frequency (%)
𝑬	4	17.4%
𝑶	2	8.7%
𝑰	2	8.7%
𝑳	2	8.7%
𝑻	1	4.3%
𝑴	1	4.3%
𝑩	1	4.3%
𝑿	1	4.3%
𝑪	1	4.3%
𝑼	1	4.3%
Other values (7)	7	30.4%

Sup Arrows B

Value	Count	Frequency (%)
⤵	2	100.0%

Geometric Shapes Ext

Value	Count	Frequency (%)
🟠	1	33.3%
🟣	1	33.3%
🟢	1	33.3%

Letterlike Symbols

Value	Count	Frequency (%)
™	1	100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	26.3 KiB

JohnLegere	3351

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	33510
Distinct characters	8
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	JohnLegere
2nd row	JohnLegere
3rd row	JohnLegere
4th row	JohnLegere
5th row	JohnLegere

Common Values

Value	Count	Frequency (%)
JohnLegere	3351	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
johnlegere	3351	100.0%

Most occurring characters

Value	Count	Frequency (%)
e	10053	30.0%
J	3351	10.0%
o	3351	10.0%
h	3351	10.0%
n	3351	10.0%
L	3351	10.0%
g	3351	10.0%
r	3351	10.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	26808	80.0%
Uppercase Letter	6702	20.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	10053	37.5%
o	3351	12.5%
h	3351	12.5%
n	3351	12.5%
g	3351	12.5%
r	3351	12.5%

Uppercase Letter

Value	Count	Frequency (%)
J	3351	50.0%
L	3351	50.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	33510	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	10053	30.0%
J	3351	10.0%
o	3351	10.0%
h	3351	10.0%
n	3351	10.0%
L	3351	10.0%
g	3351	10.0%
r	3351	10.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	33510	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	10053	30.0%
J	3351	10.0%
o	3351	10.0%
h	3351	10.0%
n	3351	10.0%
L	3351	10.0%
g	3351	10.0%
r	3351	10.0%

mentions
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	28
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.021486124

Minimum	0
Maximum	38
Zeros	1017
Zeros (%)	30.3%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	2
Q3	4
95-th percentile	11
Maximum	38
Range	38
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	3.900266084
Coefficient of variation (CV)	1.290843619
Kurtosis	7.917982874
Mean	3.021486124
Median Absolute Deviation (MAD)	2
Skewness	2.340774776
Sum	10125
Variance	15.21207552
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=28)

Value	Count	Frequency (%)
0	1017	30.3%
1	536	16.0%
2	448	13.4%
3	330	9.8%
4	237	7.1%
5	187	5.6%
7	121	3.6%
6	116	3.5%
9	73	2.2%
8	65	1.9%
Other values (18)	221	6.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1017	30.3%
1	536	16.0%
2	448	13.4%
3	330	9.8%
4	237	7.1%
5	187	5.6%
6	116	3.5%
7	121	3.6%
8	65	1.9%
9	73	2.2%

Value	Count	Frequency (%)
38	1	< 0.1%
30	1	< 0.1%
27	1	< 0.1%
24	6	0.2%
23	7	0.2%
22	2	0.1%
21	4	0.1%
20	2	0.1%
19	5	0.1%
18	6	0.2%

hashtags
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	30
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.626977022

Minimum	0
Maximum	33
Zeros	771
Zeros (%)	23.0%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	3
Q3	5
95-th percentile	11
Maximum	33
Range	33
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	3.981888897
Coefficient of variation (CV)	1.097853356
Kurtosis	6.801975584
Mean	3.626977022
Median Absolute Deviation (MAD)	2
Skewness	2.00937079
Sum	12154
Variance	15.85543919
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=30)

Value	Count	Frequency (%)
0	771	23.0%
1	477	14.2%
2	422	12.6%
3	356	10.6%
4	284	8.5%
5	245	7.3%
6	191	5.7%
7	136	4.1%
8	111	3.3%
9	81	2.4%
Other values (20)	277	8.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	771	23.0%
1	477	14.2%
2	422	12.6%
3	356	10.6%
4	284	8.5%
5	245	7.3%
6	191	5.7%
7	136	4.1%
8	111	3.3%
9	81	2.4%

Value	Count	Frequency (%)
33	2	0.1%
32	2	0.1%
31	1	< 0.1%
30	1	< 0.1%
29	1	< 0.1%
26	1	< 0.1%
24	1	< 0.1%
22	2	0.1%
21	4	0.1%
20	4	0.1%

cashtags
Real number (ℝ_≥0)

ZEROS

Distinct	14
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.06893464637

Minimum	0
Maximum	16
Zeros	3275
Zeros (%)	97.7%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	16
Range	16
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.6684971107
Coefficient of variation (CV)	9.697548996
Kurtosis	267.3476388
Mean	0.06893464637
Median Absolute Deviation (MAD)	0
Skewness	14.87541915
Sum	231
Variance	0.4468883871
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=14)

Value	Count	Frequency (%)
0	3275	97.7%
1	38	1.1%
2	10	0.3%
3	9	0.3%
6	4	0.1%
4	4	0.1%
7	3	0.1%
8	2	0.1%
15	1	< 0.1%
10	1	< 0.1%
Other values (4)	4	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	3275	97.7%
1	38	1.1%
2	10	0.3%
3	9	0.3%
4	4	0.1%
5	1	< 0.1%
6	4	0.1%
7	3	0.1%
8	2	0.1%
10	1	< 0.1%

Value	Count	Frequency (%)
16	1	< 0.1%
15	1	< 0.1%
12	1	< 0.1%
11	1	< 0.1%
10	1	< 0.1%
8	2	0.1%
7	3	0.1%
6	4	0.1%
5	1	< 0.1%
4	4	0.1%

video
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	23
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.936436885

Minimum	0
Maximum	25
Zeros	670
Zeros (%)	20.0%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	2
Q3	4
95-th percentile	9
Maximum	25
Range	25
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.981007596
Coefficient of variation (CV)	1.015178501
Kurtosis	4.332311361
Mean	2.936436885
Median Absolute Deviation (MAD)	2
Skewness	1.722116784
Sum	9840
Variance	8.886406285
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=23)

Value	Count	Frequency (%)
0	670	20.0%
1	636	19.0%
2	537	16.0%
3	452	13.5%
4	324	9.7%
5	204	6.1%
6	139	4.1%
7	108	3.2%
8	86	2.6%
9	56	1.7%
Other values (13)	139	4.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	670	20.0%
1	636	19.0%
2	537	16.0%
3	452	13.5%
4	324	9.7%
5	204	6.1%
6	139	4.1%
7	108	3.2%
8	86	2.6%
9	56	1.7%

Value	Count	Frequency (%)
25	1	< 0.1%
21	1	< 0.1%
20	2	0.1%
19	2	0.1%
18	2	0.1%
17	4	0.1%
16	1	< 0.1%
15	6	0.2%
14	8	0.2%
13	13	0.4%

photos
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	22
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.07191883

Minimum	0
Maximum	25
Zeros	787
Zeros (%)	23.5%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	2
Q3	4
95-th percentile	9
Maximum	25
Range	25
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	3.230806358
Coefficient of variation (CV)	1.051722567
Kurtosis	3.806946404
Mean	3.07191883
Median Absolute Deviation (MAD)	2
Skewness	1.645286343
Sum	10294
Variance	10.43810972
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=22)

Value	Count	Frequency (%)
0	787	23.5%
1	566	16.9%
2	433	12.9%
3	411	12.3%
4	323	9.6%
5	210	6.3%
6	168	5.0%
7	130	3.9%
8	91	2.7%
9	69	2.1%
Other values (12)	163	4.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	787	23.5%
1	566	16.9%
2	433	12.9%
3	411	12.3%
4	323	9.6%
5	210	6.3%
6	168	5.0%
7	130	3.9%
8	91	2.7%
9	69	2.1%

Value	Count	Frequency (%)
25	1	< 0.1%
21	3	0.1%
20	4	0.1%
18	3	0.1%
17	5	0.1%
16	6	0.2%
15	6	0.2%
14	9	0.3%
13	22	0.7%
12	18	0.5%

urls
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	22
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.178752611

Minimum	0
Maximum	32
Zeros	613
Zeros (%)	18.3%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	2
Q3	3
95-th percentile	6
Maximum	32
Range	32
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	2.229913805
Coefficient of variation (CV)	1.023481874
Kurtosis	23.78758282
Mean	2.178752611
Median Absolute Deviation (MAD)	1
Skewness	3.274763458
Sum	7301
Variance	4.972515578
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=22)

Value	Count	Frequency (%)
1	913	27.2%
2	726	21.7%
0	613	18.3%
3	468	14.0%
4	272	8.1%
5	146	4.4%
6	89	2.7%
7	47	1.4%
8	28	0.8%
9	15	0.4%
Other values (12)	34	1.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	613	18.3%
1	913	27.2%
2	726	21.7%
3	468	14.0%
4	272	8.1%
5	146	4.4%
6	89	2.7%
7	47	1.4%
8	28	0.8%
9	15	0.4%

Value	Count	Frequency (%)
32	1	< 0.1%
24	2	0.1%
23	1	< 0.1%
22	1	< 0.1%
19	1	< 0.1%
17	1	< 0.1%
16	5	0.1%
15	1	< 0.1%
13	4	0.1%
12	3	0.1%

replies_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	589
Distinct (%)	17.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	181.2649955

Minimum	0
Maximum	9914
Zeros	14
Zeros (%)	0.4%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	7
Q1	40
median	90
Q3	174
95-th percentile	555.5
Maximum	9914
Range	9914
Interquartile range (IQR)	134

Descriptive statistics

Standard deviation	448.6132326
Coefficient of variation (CV)	2.474902732
Kurtosis	181.1043762
Mean	181.2649955
Median Absolute Deviation (MAD)	60
Skewness	11.47444896
Sum	607419
Variance	201253.8324
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
5	33	1.0%
30	30	0.9%
12	28	0.8%
20	27	0.8%
49	27	0.8%
14	27	0.8%
21	26	0.8%
53	26	0.8%
43	26	0.8%
7	25	0.7%
Other values (579)	3076	91.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	14	0.4%
1	22	0.7%
2	24	0.7%
3	23	0.7%
4	17	0.5%
5	33	1.0%
6	24	0.7%
7	25	0.7%
8	18	0.5%
9	16	0.5%

Value	Count	Frequency (%)
9914	1	< 0.1%
8763	1	< 0.1%
7854	1	< 0.1%
6540	1	< 0.1%
6217	1	< 0.1%
6201	1	< 0.1%
5836	1	< 0.1%
4863	1	< 0.1%
4544	1	< 0.1%
3561	1	< 0.1%

retweets_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct	648
Distinct (%)	19.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	226.853775

Minimum	0
Maximum	35956
Zeros	11
Zeros (%)	0.3%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	6
Q1	36
median	100
Q3	202
95-th percentile	639.5
Maximum	35956
Range	35956
Interquartile range (IQR)	166

Descriptive statistics

Standard deviation	982.4743157
Coefficient of variation (CV)	4.330870473
Kurtosis	782.5084178
Mean	226.853775
Median Absolute Deviation (MAD)	74
Skewness	24.98091088
Sum	760187
Variance	965255.781
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3	36	1.1%
9	36	1.1%
27	31	0.9%
4	31	0.9%
8	30	0.9%
23	29	0.9%
19	29	0.9%
5	29	0.9%
7	28	0.8%
16	27	0.8%
Other values (638)	3045	90.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	11	0.3%
1	17	0.5%
2	20	0.6%
3	36	1.1%
4	31	0.9%
5	29	0.9%
6	27	0.8%
7	28	0.8%
8	30	0.9%
9	36	1.1%

Value	Count	Frequency (%)
35956	1	< 0.1%
29014	1	< 0.1%
17991	1	< 0.1%
11029	1	< 0.1%
7789	1	< 0.1%
7595	1	< 0.1%
7537	1	< 0.1%
6914	1	< 0.1%
6523	1	< 0.1%
6145	1	< 0.1%

likes_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	1852
Distinct (%)	55.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1204.185616

Minimum	0
Maximum	46692
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	0
5-th percentile	62
Q1	372.5
median	801
Q3	1393.5
95-th percentile	3506
Maximum	46692
Range	46692
Interquartile range (IQR)	1021

Descriptive statistics

Standard deviation	1931.216281
Coefficient of variation (CV)	1.603752989
Kurtosis	144.8121332
Mean	1204.185616
Median Absolute Deviation (MAD)	493
Skewness	9.312195808
Sum	4035226
Variance	3729596.324
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3	9	0.3%
161	8	0.2%
139	7	0.2%
801	7	0.2%
106	7	0.2%
43	7	0.2%
261	7	0.2%
773	7	0.2%
27	7	0.2%
252	6	0.2%
Other values (1842)	3279	97.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	< 0.1%
2	3	0.1%
3	9	0.3%
4	2	0.1%
5	1	< 0.1%
6	4	0.1%
7	3	0.1%
8	1	< 0.1%
9	4	0.1%
10	2	0.1%

Value	Count	Frequency (%)
46692	1	< 0.1%
28297	1	< 0.1%
27888	1	< 0.1%
25605	1	< 0.1%
22471	1	< 0.1%
22216	1	< 0.1%
21533	1	< 0.1%
20116	1	< 0.1%
19740	1	< 0.1%
16377	1	< 0.1%

number of tweets
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	55
Distinct (%)	1.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	10.87347061

Minimum	1
Maximum	133
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	4
median	9
Q3	15
95-th percentile	28
Maximum	133
Range	132
Interquartile range (IQR)	11

Descriptive statistics

Standard deviation	9.187602801
Coefficient of variation (CV)	0.8449558687
Kurtosis	11.1544636
Mean	10.87347061
Median Absolute Deviation (MAD)	6
Skewness	1.955514747
Sum	36437
Variance	84.41204523
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	286	8.5%
2	268	8.0%
3	240	7.2%
7	182	5.4%
5	172	5.1%
4	171	5.1%
6	167	5.0%
8	166	5.0%
10	155	4.6%
12	148	4.4%
Other values (45)	1396	41.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	286	8.5%
2	268	8.0%
3	240	7.2%
4	171	5.1%
5	172	5.1%
6	167	5.0%
7	182	5.4%
8	166	5.0%
9	136	4.1%
10	155	4.6%

Value	Count	Frequency (%)
133	1	< 0.1%
66	1	< 0.1%
61	1	< 0.1%
59	1	< 0.1%
56	1	< 0.1%
53	1	< 0.1%
50	1	< 0.1%
49	2	0.1%
47	2	0.1%
46	3	0.1%

price
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	2742
Distinct (%)	81.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	78.4167666

Minimum	44.5
Maximum	149.4100037
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	26.3 KiB

Quantile statistics

Minimum	44.5
5-th percentile	52.85666529
Q1	61.96000036
median	68.41666667
Q3	82.0749979
95-th percentile	133.0133355
Maximum	149.4100037
Range	104.9100037
Interquartile range (IQR)	20.11499755

Descriptive statistics

Standard deviation	25.15097148
Coefficient of variation (CV)	0.3207346154
Kurtosis	0.5569027693
Mean	78.4167666
Median Absolute Deviation (MAD)	8.796666463
Skewness	1.277454835
Sum	262774.5849
Variance	632.5713662
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
61.50999832	7	0.2%
63.27000046	6	0.2%
62.38999939	6	0.2%
59.75	6	0.2%
62.47000122	6	0.2%
57.25	6	0.2%
61.25	5	0.1%
63.88999939	5	0.1%
63.84000015	5	0.1%
75.47000122	5	0.1%
Other values (2732)	3294	98.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
44.5	2	0.1%
44.63999939	1	< 0.1%
45.00999832	1	< 0.1%
45.03000005	1	< 0.1%
45.06999969	1	< 0.1%
45.27000046	1	< 0.1%
45.29999924	1	< 0.1%
45.34000015	1	< 0.1%
45.34999847	1	< 0.1%
45.44666672	1	< 0.1%

Value	Count	Frequency (%)
149.4100037	1	< 0.1%
149.3399963	1	< 0.1%
148.8999939	1	< 0.1%
148.4900055	1	< 0.1%
148.3399963	1	< 0.1%
148.0899963	1	< 0.1%
148.0399933	1	< 0.1%
147.8933309	1	< 0.1%
147.8100026	1	< 0.1%
147.7700043	1	< 0.1%

percent change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	3309
Distinct (%)	98.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.000302198429

Minimum	-0.06843111568
Maximum	0.1084821756
Zeros	38
Zeros (%)	1.1%
Negative	1581
Negative (%)	47.2%
Memory size	26.3 KiB

Quantile statistics

Minimum	-0.06843111568
5-th percentile	-0.01682789332
Q1	-0.004948755432
median	0.0003068140092
Q3	0.005462388263
95-th percentile	0.01696509965
Maximum	0.1084821756
Range	0.1769132913
Interquartile range (IQR)	0.01041114369

Descriptive statistics

Standard deviation	0.01139192844
Coefficient of variation (CV)	37.69684864
Kurtosis	8.12829414
Mean	0.000302198429
Median Absolute Deviation (MAD)	0.005192809984
Skewness	0.4855065572
Sum	1.012666936
Variance	0.0001297760335
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	38	1.1%
0.002955019875	2	0.1%
0.007478506125	2	0.1%
0.003448835516	2	0.1%
0.003786595981	2	0.1%
0.0003052922606	2	0.1%
-0.01533498499	1	< 0.1%
0.005613581119	1	< 0.1%
0.006628298967	1	< 0.1%
-0.01403897731	1	< 0.1%
Other values (3299)	3299	98.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-0.06843111568	1	< 0.1%
-0.06812496185	1	< 0.1%
-0.06413063548	1	< 0.1%
-0.05124349869	1	< 0.1%
-0.04950270397	1	< 0.1%
-0.04930583099	1	< 0.1%
-0.04837080115	1	< 0.1%
-0.04831318375	1	< 0.1%
-0.04786215792	1	< 0.1%
-0.04748369984	1	< 0.1%

Value	Count	Frequency (%)
0.1084821756	1	< 0.1%
0.07765805655	1	< 0.1%
0.07647350037	1	< 0.1%
0.06537738944	1	< 0.1%
0.06259195553	1	< 0.1%
0.05939567363	1	< 0.1%
0.05787496567	1	< 0.1%
0.05583080942	1	< 0.1%
0.05499112949	1	< 0.1%
0.05467628694	1	< 0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	3.5 KiB

no change	2662
rise	349
drop	340

Length

Max length	9
Median length	9
Mean length	7.971948672
Min length	4

Characters and Unicode

Total characters	26714
Distinct characters	13
Distinct categories	2 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	no change
2nd row	no change
3rd row	no change
4th row	no change
5th row	no change

Common Values

Value	Count	Frequency (%)
no change	2662	79.4%
rise	349	10.4%
drop	340	10.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
no	2662	44.3%
change	2662	44.3%
rise	349	5.8%
drop	340	5.7%

Most occurring characters

Value	Count	Frequency (%)
n	5324	19.9%
e	3011	11.3%
o	3002	11.2%
	2662	10.0%
c	2662	10.0%
h	2662	10.0%
a	2662	10.0%
g	2662	10.0%
r	689	2.6%
i	349	1.3%
Other values (3)	1029	3.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	24052	90.0%
Space Separator	2662	10.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	5324	22.1%
e	3011	12.5%
o	3002	12.5%
c	2662	11.1%
h	2662	11.1%
a	2662	11.1%
g	2662	11.1%
r	689	2.9%
i	349	1.5%
s	349	1.5%
Other values (2)	680	2.8%

Space Separator

Value	Count	Frequency (%)
	2662	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	24052	90.0%
Common	2662	10.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	5324	22.1%
e	3011	12.5%
o	3002	12.5%
c	2662	11.1%
h	2662	11.1%
a	2662	11.1%
g	2662	11.1%
r	689	2.9%
i	349	1.5%
s	349	1.5%
Other values (2)	680	2.8%

Common

Value	Count	Frequency (%)
	2662	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	26714	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	5324	19.9%
e	3011	11.3%
o	3002	11.2%
	2662	10.0%
c	2662	10.0%
h	2662	10.0%
a	2662	10.0%
g	2662	10.0%
r	689	2.6%
i	349	1.3%
Other values (3)	1029	3.9%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	date	tweet	username	mentions	hashtags	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
0	2016-08-23 09:30:00	@Azphxmex @art_lucero - please look into it ASAP @mgcarley @TMobile You’re wearing the wrong color ;) @tsukinoxo @TMobile do it!!! @mikusz @ATT @TMobile @ATTCares Good choice :)))) @Abruuh_ @TMobile Welcome back! Ready to #GetThanked?! @Mikesmith2317 Come on over!! @Prttyawsmcool Email me! John.legere@t-mobile.com Well, there goes my productivity for the day https://t.co/vxZTn2raCG The future of watching the #Olympics is on mobile, but I think we already knew that! ;) https://t.co/FCsSqRPJMN @isnotgraham @art_lucero john.legere@t-mobile.com @isnotgraham @art_lucero can you email me? Oooo, I need this! @thenorthface, how do I get one in #magenta?!? #gadgetlover https://t.co/xWKZXcSd5X More things to #BingeOn from @Netflix!! https://t.co/Y8IikGMJH9 Hey @MichaelPhelps - I've got your back!! :)))) Watch this: https://t.co/B1XR0MHSjk https://t.co/vnsLUmLuCg Interesting arguments for/against wearables on the field. #MLB https://t.co/IRraM3onkr Use these tips to get tickets, and @tmobile to get free wifi while you’re flying. #tmobilewingman https://t.co/jwffPWCP9F	JohnLegere	6	7	1	1	7	52	100	564	16	46.959999	0.000000	no change
1	2016-08-23 16:00:00	@silods yes! Very proud of @UMassAmherst and to have studied there! https://t.co/sjK4IbTy7z @iSocialFanz possibly :)) This is hilarious, but also brilliant!! https://t.co/9wtLj2J0qa I might wear one if it were #magenta, @Xbox…. ;) https://t.co/Mk3EI3Q37R #TuesdayMotivation: Today is #TMobileTuesdays & you can #GetThanked w/ awesome prizes from @Orbitz!! #TMobileTuesdays is giving away a chance to win tickets to anywhere in North America!! Where would u go?!? https://t.co/8FGkPmbUYG	JohnLegere	3	5	1	1	3	274	212	929	7	46.830002	-0.002768	no change
2	2016-08-24 09:30:00	@Woofdah @TMobile What are you going to rent?! @FreelancerJenny 👍🏼 @CURLZ_92 The best day of the week!! @SCAFFBEEZYjr They should switch! @lennahs @TMobile good choice! I think this means I technically have a gold medal ;) https://t.co/CC9KXb07Ch Add this to the list of ways not to impress a date https://t.co/gIGqMvBd1t So great!! You need to hear what we’re cooking up for @TMobileAtWork customers, I think you’d like it! https://t.co/lTCXDP5ero Watching #TheProfit, love the show. @MarcusLemonis does this shirt come in magenta?! https://t.co/S262GTf1OQ @irusanovschi stop thinking and come over!!!! @_Lucky45 i read all email and follow up... No boiler rooms @TMobile :) It’s #TravelTuesday & #TMobileTuesdays!! Coincidence?? Nope!! Now, go try to win cool @Orbitz stuff!! I wonder what would come up if you draw @ATT?! #DeatthStar LOL https://t.co/MvuHgF7QxD Today I dress in what I love! All @TMobile (well I do every day)! ...it's also a great way to meet people :)) https://t.co/iqQAaTLXnm Flying out of Seattle on a beautiful day... Many stops ahead in next week..a lot of @TMobile people to thank! https://t.co/IQ5n8Yz6L6	JohnLegere	7	4	3	3	4	127	186	1453	15	46.660000	-0.003630	no change
3	2016-08-24 16:00:00	Sunscreen doesn’t smell great, but I don’t think fried chicken-scented sunscreen is any better… https://t.co/VT6qgGquMK Our thoughts & prayers are with 🇮🇹. Fees for @TMobile custs to call/text to/from Italy will be waived until 8/28. https://t.co/wcw2ZY98aL I read every email I get, no matter what :))))) https://t.co/AURnzcFZV5 Is this what the future looks like??? https://t.co/3KU7cnqv5f	JohnLegere	1	0	0	0	4	70	215	712	4	46.209999	-0.009644	no change
4	2016-08-25 09:30:00	@MillerPhelan Thank you!! @LIVEWlRED You should! @Beverly330 @TMobile welcome!! @petersterne I ❤️ @TMobile ;) @ArimHerrera Switch to @TMobile! :)))) @ItsTonyWhite There’s always next week! @AlienReconstar Set reminders! ;) Great day for a run in @CentralParkNYC of course in all @TMobile #Magenta https://t.co/pK5yZZQZmB	JohnLegere	4	1	1	1	0	27	15	240	8	46.310001	0.002164	no change
5	2016-08-25 16:00:00	#TBT to that time we surprised the country with #uncarrier12!! Just last week!! https://t.co/k74aVvYxwL @TheresaRockFace @TMobile @ATT Definitely come back to @tmobile! :) Who knew you needed a to-do list hack!? Good tips, though! 📝 https://t.co/9cUGS5OJiy Flying today and still not convinced that you can't run on clouds! Looks like you should be able to doesn't it? :) https://t.co/5qCk33yaWJ But where's the magenta-wearing #CEO emoji??? https://t.co/IuhVb06vbH How do I get one of these for my office?!? #ineedit https://t.co/4RkGxhwIN4 We could send @LegereDoll for a test visit!! ;) https://t.co/1m4WAhgtl0	JohnLegere	2	4	1	1	5	103	133	840	7	46.279999	-0.000648	no change
6	2016-08-26 09:30:00	@imjus_selfmade @Whiplashjay_ @TMobile deal! @ocmarcelle Good choice :) @Iamsolomon_c #Magenta FTW! @ZacharyKhan3 ;) @Whiplashjay_ Switch to @TMobile!! @MrsNiaB Tuesday’s are the best now!! Does this mean we could see #uber deliveries?! Hmmmm…. https://t.co/3iwj2Bx3gU	JohnLegere	3	2	0	0	1	31	44	163	7	46.419998	0.003025	no change
7	2016-08-26 16:00:00	LIVE on #Periscope: @TMobile #CEO in #Birmingham! Let’s meet the amazing @TMobile team here!! https://t.co/qRq66z6OnH Happy #NationalDogDay & #RuffFriday! No #LegereDog yet, but go adopt Apple at @SeattleHumane https://t.co/LYRsyxal0f https://t.co/uU3RVGUWrl My #FridayFeeling!! What’s yours? https://t.co/bu25nlaa1b Up up and away... Everyday!!! Good bye Seattle and next stop Birmingham!!!! https://t.co/XzGlEjmOoM Or you could just use a #slowcooker, like me! https://t.co/OQOEBPyhyQ @KingspiritTravl But, #IAmBatman!!!	JohnLegere	3	9	3	3	3	81	194	873	6	45.959999	-0.009910	no change
8	2016-08-27 09:30:00	@rosyna @evepeyser not blocked any more :) , oops or did I ruin your membership to the who blocked you club? :) sorry either way 🤔 This will be great for future #LegereDog!! https://t.co/qLez4HOT3o .@trackjenny @TMobile Glad @TMobile could help you enjoy #Rio!! @monikarun @TMobile @ATT we have free things for all @MiaStJohnBoxer @Zeola3 @kristoffstjohn1 i would be glad to help any way I can @HenryBoomsma Where ya headed?! @KeishKeish1 You guys are awesome!! @KeishKeish1 @theSamsungSide ;) @ChristinaBhamAl Thanks for having me!! @thutcherson4 @jgebing Nice to hang out with all of you!! @ProfStuckey @TMobile It’s going to be great!! You have until Sunday at 5pm ET & then I’ll pick 5 winners!! Good luck!! Terms & Conditions: https://t.co/qQhmGVJE70 Wow! U all ❤️ @ElectricZooNY! If u want a pair of VIP tix + ferry passes use #TMOEZoo & tag the artist ur most excited to see!! Must be 21+ Did you guys know that @TMobile is sponsoring @ElectricZooNY next weekend?!? I’ll be there!! RT if u wanna come!! I might have extra tix ;) @Zeola3 @MiaStJohnBoxer I agree. Let me know how I can help @Xaighen @art_lucero yes!!!! Today I was in #Birmingham to say #ThankYou to the best team on the 🌎!! I 💗 them!! https://t.co/z0JSTzDuau If they keep updating, I’m going to guess FOREVER… https://t.co/oFZlreenca	JohnLegere	7	5	1	4	3	264	424	1709	18	46.493332	0.011604	rise
9	2016-08-27 16:00:00	Want a pair of VIP tix + ferry passes to @ElectricZooNY?!? U can enter until tmrw!! Go!!!! https://t.co/qQhmGVJE70 https://t.co/Wz3e4rTDLh After a busy week go the countryside, breathe deep, ok now back to work :))) https://t.co/7Bl1CatKgC Can I get a magenta plaid speaker, @WoolrichInc??? :)))) https://t.co/e7A6dd9SFe Props to our field engineers!! They run into some interesting things... 🐍 https://t.co/Aua8gIrOdD Time to check your #TMobileTuesdays app!! Next week you can save $ on gas w/ @Shell!! You might even win a new car!! ;) @awonderdj Can I help? email me! john.legere@t-mobile.com	JohnLegere	3	1	1	1	4	90	158	830	6	46.216666	-0.005951	no change

Last rows

	date	tweet	username	mentions	hashtags	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
3341	2021-07-15 16:00:00	Big congratulations to @Candace_Parker! Can’t wait to buy! https://t.co/QNmxa35kMR See you never, Fleets! https://t.co/6bATAKXEvl	JohnLegere	1	0	0	0	2	6	5	106	2	148.339996	-0.006696	no change
3342	2021-07-16 09:30:00	Congratulations to @neilbarua and the @ServiceMax team!!! https://t.co/AFaIqM1I48 More additions to Clubhouse! Who still actively uses??! https://t.co/OCwsKhscIv	JohnLegere	2	0	0	0	2	3	8	45	2	148.089996	-0.001685	no change
3343	2021-07-16 16:00:00	Trust science! It’s important to continue staying safe, the COVID-19 pandemic isn’t over yet! https://t.co/HBhDZCbvrk BUT WHERE IS THE SOLID PINK HEART?!! https://t.co/wF8LWtG6dx	JohnLegere	0	0	0	0	2	36	22	161	2	149.410004	0.008914	no change
3344	2021-07-17 09:30:00	Another step forward for accessibility on Twitter. https://t.co/kiC4DpDOL2	JohnLegere	0	0	0	0	1	1	8	22	1	147.893331	-0.010151	no change
3345	2021-07-17 16:00:00	I finally found a way to go to the beach without having to go ON the beach (except to 🏊 in ocean of course) 🌞 🌊 https://t.co/IPXvQZVijB But how will I know when I’m going to get scammed?!! https://t.co/L0slvP3HPG My kind of charcuterie board! https://t.co/6BhW2V747Z Happy #WorldEmojiDay! Which is your favorite?? https://t.co/Px8HHWYOvE	JohnLegere	0	1	1	2	3	45	24	340	4	147.810003	-0.000563	no change
3346	2021-07-18 16:00:00	Who’s subscribing?? https://t.co/bZr4a84P2X Hey New Yorkers – need an officiant?! https://t.co/PbBecmjgu0 is it possible that the ocean makes the morning coffee ☕️ even better than it's normal great? 😊 https://t.co/43jhIroufd	JohnLegere	0	0	1	1	2	141	27	1218	3	146.210002	-0.010066	no change
3347	2021-07-19 09:30:00	Number 16 is a movie night game changer!! https://t.co/9YJS0H383P	JohnLegere	0	0	0	0	1	5	2	32	1	147.500000	0.008823	no change
3348	2021-07-19 16:00:00	Gorgeous! Maybe I should charter a magenta rocket. https://t.co/jx2ZL0gX6Q So I'm about to go for a run on the beach.....am i the only one that doesn't think these ☁️ mean 🌧 ☔️ is coming? https://t.co/fMxzuOZ0nF Always love a good Quesarito https://t.co/KaiMmXEc5q What I’ve learned from @Lifehacker is that I do EVERYTHING wrong! https://t.co/xAbzXxZhqZ	JohnLegere	1	0	1	2	3	25	17	236	4	144.610001	-0.019593	drop
3349	2021-07-20 09:30:00	Call on John is POSTPONED for today but tune in on Thursday, July 22nd at 12 PM ET on Instagram Stories for an extra special Call on John! What do you want to see me give away?? https://t.co/4P3hucQ3io	JohnLegere	0	0	1	1	0	7	3	18	1	144.789993	0.001245	no change
3350	2021-07-20 16:00:00	WOW this looks so delicious! https://t.co/hnOKodchlg Up up and away! https://t.co/jvRcpkqng3	JohnLegere	0	0	0	0	2	28	9	115	2	144.399994	-0.002694	no change

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